




'''simple docstring'''




from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


lowercase__  							=					transforms.Compose(
    [
        transforms.Resize((2_56, 2_56)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)
def      __snake_case       (   lowercase   :							int  ):
					if isinstance(_A			, torch.Tensor  ):
										return image
					elif isinstance(_A			, PIL.Image.Image  ):
										snake_case_	=      [image]

					snake_case_	=      [trans(img.convert("RGB"  )  ) for img in image]
					snake_case_	=      torch.stack(_A  )
					return image






class      UpperCAmelCase_     (  lowerCamelCase__     ):


	"""simple docstring"""


	def __init__(						self						, UpperCAmelCase_						, UpperCAmelCase_				):
						super().__init__()

						# make sure scheduler can always be converted to DDIM
						snake_case_	=      DDIMScheduler.from_config(scheduler.config				)

						self.register_modules(unet=__snake_case						, scheduler=__snake_case				)







	def  _lowercase    (						self						, UpperCAmelCase_				):
						if strength < 0 or strength > 1:
											raise ValueError(f'''The value of strength should in [0.0, 1.0] but is {strength}'''				)







	def  _lowercase    (						self						, UpperCAmelCase_						, UpperCAmelCase_						, UpperCAmelCase_				):
						# get the original timestep using init_timestep
						snake_case_	=      min(int(num_inference_steps * strength				)						, __snake_case				)

						snake_case_	=      max(num_inference_steps - init_timestep						, 0				)
						snake_case_	=      self.scheduler.timesteps[t_start:]

						return timesteps, num_inference_steps - t_start







	def  _lowercase    (						self						, UpperCAmelCase_						, UpperCAmelCase_						, UpperCAmelCase_						, UpperCAmelCase_						, UpperCAmelCase_						, UpperCAmelCase_=None				):
						if not isinstance(__snake_case						, (torch.Tensor, PIL.Image.Image, list)				):
											raise ValueError(
											    f'''`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(__snake_case				)}'''				)

						snake_case_	=      image.to(device=__snake_case						, dtype=__snake_case				)

						if isinstance(__snake_case						, __snake_case				) and len(__snake_case				) != batch_size:
											raise ValueError(
											    f'''You have passed a list of generators of length {len(__snake_case				)}, but requested an effective batch'''
											    f''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''				)

						snake_case_	=      init_latents.shape
						snake_case_	=      randn_tensor(__snake_case						, generator=__snake_case						, device=__snake_case						, dtype=__snake_case				)

						# get latents
						print("add noise to latents at timestep"						, __snake_case				)
						snake_case_	=      self.scheduler.add_noise(__snake_case						, __snake_case						, __snake_case				)
						snake_case_	=      init_latents

						return latents







	@torch.no_grad()
	def __call__(						self						, UpperCAmelCase_ = None						, UpperCAmelCase_ = 0.8						, UpperCAmelCase_ = 1						, UpperCAmelCase_ = None						, UpperCAmelCase_ = 0.0						, UpperCAmelCase_ = 50						, UpperCAmelCase_ = None						, UpperCAmelCase_ = "pil"						, UpperCAmelCase_ = True						, ):
						self.check_inputs(__snake_case				)

						# 2. Preprocess image
						snake_case_	=      preprocess(__snake_case				)

						# 3. set timesteps
						self.scheduler.set_timesteps(__snake_case						, device=self.device				)
						snake_case_	=      self.get_timesteps(__snake_case						, __snake_case						, self.device				)
						snake_case_	=      timesteps[:1].repeat(__snake_case				)

						# 4. Prepare latent variables
						snake_case_	=      self.prepare_latents(__snake_case						, __snake_case						, __snake_case						, self.unet.dtype						, self.device						, __snake_case				)
						snake_case_	=      latents

						# 5. Denoising loop
						for t in self.progress_bar(__snake_case				):
											# 1. predict noise model_output
											snake_case_	=      self.unet(__snake_case						, __snake_case				).sample

											# 2. predict previous mean of image x_t-1 and add variance depending on eta
											# eta corresponds to η in paper and should be between [0, 1]
											# do x_t -> x_t-1
											snake_case_	=      self.scheduler.step(
											    __snake_case						, __snake_case						, __snake_case						, eta=__snake_case						, use_clipped_model_output=__snake_case						, generator=__snake_case						, ).prev_sample

						snake_case_	=      (image / 2 + 0.5).clamp(0						, 1				)
						snake_case_	=      image.cpu().permute(0						, 2						, 3						, 1				).numpy()
						if output_type == "pil":
											snake_case_	=      self.numpy_to_pil(__snake_case				)

						if not return_dict:
											return (image, latent_timestep.item())

						return ImagePipelineOutput(images=__snake_case				)










'''simple docstring'''






from __future__ import annotations

lowerCAmelCase:   str					=		'Muhammad Umer Farooq'
lowerCAmelCase:   List[str]					=		'MIT'
lowerCAmelCase:   Tuple					=		'1.0.0'
lowerCAmelCase:   List[Any]					=		'Muhammad Umer Farooq'
lowerCAmelCase:   Optional[Any]					=		'contact@muhammadumerfarooq.me'
lowerCAmelCase:   Dict					=		'Alpha'

import re
from html.parser import HTMLParser
from urllib import parse

import requests

class 				a__(   lowerCamelCase__      ):







    def __init__( self					:    Dict		,     __snake_case					:    str      ):
      super().__init__()
      a		:   list[str]	  =	[]
      a		:   List[Any]	  =	domain







    def      lowercase_			( self					:    Dict		,     __snake_case					:    str		,     __snake_case					:    list[tuple[str, str | None]]      ):
      # Only parse the 'anchor' tag.
      if tag == "a":
        # Check the list of defined attributes.
        for name, value in attrs:
          # If href is defined, and not empty nor # print it.
          if name == "href" and value != "#" and value != "":
            # If not already in urls.
            if value not in self.urls:
              a		:   Tuple	  =	parse.urljoin(self.domain		,     __snake_case      )
              self.urls.append(__snake_case      )
def  lowerCamelCase__			(		_A							):
  return ".".join(get_sub_domain_name(_A							).split('.'							)[-2:]							)
def  lowerCamelCase__			(		_A							):
  return parse.urlparse(_A							).netloc
def  lowerCamelCase__			(		_A = "https://github.com"							):
  a		:   Any	  =	get_domain_name(_A							)

  # Initialize the parser
  a		:   Tuple	  =	Parser(_A							)

  try:
    # Open URL
    a		:   List[Any]	  =	requests.get(_A							)

    # pass the raw HTML to the parser to get links
    parser.feed(r.text							)

    # Get links and loop through
    a		:   Union[str, Any]	  =	set()
    for link in parser.urls:
      # open URL.
      # read = requests.get(link)
      try:
        a		:   int	  =	requests.get(_A							)
        # Get the valid email.
        a		:   Optional[Any]	  =	re.findall('[a-zA-Z0-9]+@' + domain					,     read.text							)
        # If not in list then append it.
        for email in emails:
          valid_emails.add(_A							)
      except ValueError:
        pass
  except ValueError:
    raise SystemExit(1							)

  # Finally return a sorted list of email addresses with no duplicates.
  return sorted(_A							)


if __name__ == "__main__":
     lowerCAmelCase:   Any					=		emails_from_url('https://github.com')
     print(F"{len(emails)} emails found:")
     print('\n'.join(sorted(emails)))